recommendations
OfficialDeliver deeply personalized recommendations.
Product & Management#personalization#recommendations#api-integration#context-building#explainability#confidence-scoring
Authortasteray
Version1.0.0
Installs0
System Documentation
What problem does it solve?
Helps agents turn conversational user data into ranked, personalized recommendations across movies, restaurants, products, travel, and jobs so users receive suggestions that truly fit their tastes, constraints, and psychological profile.
Core Features & Use Cases
- Build rich recommendation context from conversation: preferences, profile summaries, constraints, and history to improve relevance.
- Integrate with TasteRay API endpoints to request recommendations and explanations, interpret confidence scores, and handle rate limits and errors.
- Presentation and iteration patterns to explain matches, surface caveats, and refine suggestions based on user feedback; ideal for product recommendation flows, restaurant discovery, travel planning, and hiring suggestions.
Quick Start
Ask for five personalized movie recommendations for a user who prefers dark comedies, has a 120-minute max runtime constraint, and a history including Parasite and The Lobster.
Dependency Matrix
Required Modules
None requiredComponents
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: recommendations Download link: https://github.com/tasteray/skills/archive/main.zip#recommendations Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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